1. Technical Field
The present inventions relate to a classification estimating system and classification estimating program through a selective desensitization neural network for estimating classification information including an operational decision of a subject.
2. Technical Background
In recent years, research and development on mechanical control using physiological information (biosignals) of human beings are overwhelmingly carried out. Said mechanical control is designed not only to provide assistance in power assistance to prosthetic arms and limbs, or self-help devices through assistive techniques and adaptive devices for those who are mobility challenged, but also to be expected for general application as control devices such as virtual reality devices and other human interface devices (as reflected in non-patent literature 1). In addition, said physiological information (biosignals) used in said mechanical control system comprises central nerve system information indicating change in brain wave and brain blood, and the nerve ending system information, which represents the EMG signals (muscle electrical signal, muscle action potential, EMG signals) indicating change of EMG signals (muscle potential signal, action potential) between said muscle and adjacent skin (as reflected in non-patent literature 1) that generate when muscle contracts.
It is known that said EMG signal can be observed 30-100 [ms] earlier than occurrence of said muscle contraction (as reflected in non-patent literature 2). That is to say, compared with said muscle contraction, said EMG signals can be measured with slight delay. Besides, the measuring device of said EMG signals has the features of high responsiveness (high sensitivity & response time) and high reliability of measured value, and can be provided at comparatively low cost. Hence application of said EMG signals, as physiological information, can be expected as practical and promising.
Here, two approaches are employed to measure said EMG signals, wherein the first approach applies needle electrodes that are sticked to muscle fiber on a subject (test subject, measured object) to obtain directly specific EMG signals; whereas the second approach applies electrodes (surface electrode) that are attached to skin of a subject to measure the combination of EMG signals generated by a plurality of muscle fibers, namely surface EMG signals (EMG: Electro Myo-Gram). Moreover, the approach that applies surface electrodes is preferable instead of the approach using said needle electrodes for it is regarded as less burdensome to a subject.
However, as combination of individual EMG signals, said surface EMG signals cause poor correspondence to motions (muscle action) of a subject due to their strong non-linear characteristic. In other words, it is difficult to classify said motions by means of said surface muscle EMG signals.
Therefore, in order to classify motions of a subject from the measured result of said surface EMG signals, neural net work techniques are applied to simplify a function (linear function) indicating the corresponding relationship between input and output based on distributed representation.
For example, motion classification techniques based on said surface EMG signals are described in the following non-patent literatures 1 and 2.
As shown in non-patent literatures 1, based on Back Propagation method (BP method) Multi-Layer Perceptron (MLP: Multi-Layered Perceptron), surface EMG signals obtained from three (3ch) electrodes (surface electrode and earth electrode) that attached to a subject's fore arm are input and 10 output fore arm motions are classified with an average recognition rate of 90%. Moreover, in non-patent literatures 1, it is described that surface EMG signals obtained from six (6ch) electrodes that are attached to a subject's wrist are input, and 6 kinds of output fore arm motion are classified with an average classification rate of 85%.
Said Multi-Layered Perceptron is a so called multi-layered neural network that the input, output as well as functions are expressed in distributed representation by elements (neutrons) comprising respectively an input layer, an intermediate layer and an output layer. With regard to said Multi-Layered Perceptron, each neuron of input and intermediate layer, each neuron of intermediate and output layer are connected respectively with connection weight, each neuron of intermediate layer as well as output layer are calculated as the sum of input from elements of input layer as well as intermediate layer by way of connection weight, in this way, said function is expressed as distributed representation. Moreover, Back Propagation method (BP method is a training algorithm to be used for the training of said perceptron, in which, when a set of training data namely a pair of input and output are given, the connection weight between each layer is modified in order for the output from Multi-Layered Perceptron to be correspondent to the output of training data. The detailed introduction of multi-layered perceptron based on Back Propagation method (BP method) is omitted for it has been described in not-patent literature 3.
In addition, LLGMN can as well be interpreted as a layer neutral network which comprises a statistical Gaussian Mixture Model and a probabilistic Hidden Markov Model. Besides, Gaussian mixture model is a probabilistic model that assumes linear combination of Gaussian distribution (normal distribution). Moreover, Markov Model is a stochastic process in which the occurrence probability of a current symbol depends on its immediate preceding n symbols, that is to say, Markov Model is a stochastic model in which it is assumed that the occurrence of a symbol depends on Markov process (a stochastic process having Markov property). Also, Hidden Markov Model is assumed to be a Markov process of system with unknown Parameters that estimates unknown parameters from observable information.
R-LLGMN is a LLGMN having possible connection of output between the first intermediate layer and the second intermediate layer at previous time step, namely recurrent connection. Also, the detailed explanation of R-LLGMN is omitted for it is described in Kokai publication Patent Tokukai No. 2005-11037 and non-patent literature 4.
As described in non-patent literatures 2, by means of Support Vector Machine, seven motions of fore arm (neutral, wrist flexion, wrist extension, grasp, open, wrist pronation, wrist supination) can be classified by inputting surface EMG signals based on measurement of EMG signals obtained from four channel electrodes attached on fore arm of a subject. In addition, as shown in non-patent literatures 2, seven motions of fore arm are classified with an average classification rate of 93.3%.
Here, said Support Vector Machine refers to a two class pattern classifier using a plurality of linear input elements (perceptron) as elements of the input layer. That is to say, it is a layer neutral network where the output values are determined by whether or not the sum of a plurality of linear input elements, multiplied by its respective connection weight (synaptic weight), exceeds a preset threshold value and then output values are output.
More specifically, let x (x=(x1, x2, . . . , xn)) denote the value set of n elements of input layer (input value) as input feature property vector, y denote the value of one element of the output layer (output value), ω (ω=(ω1, ω2, . . . , ωn)) denote the vector of the connection weight (connection weight vector) of each input element as well as the output element, ωT denote the transpose vector of connection weight vector ω, and h denote the threshold value, then the output value y can be defined as expressed in the following function (1).
                                                        y              =                            ⁢                              sign                ⁡                                  (                                                                                    ω                        T                                            ⁢                      x                                        -                    h                                    )                                                                                                        =                            ⁢                              sign                ⁡                                  (                                                                                    x                        1                                            ⁢                                              ω                        1                                                              +                                                                  x                        2                                            ⁢                                              ω                        2                                                              +                    …                    +                                                                  x                        n                                            ⁢                                              ω                        n                                                              -                    h                                    )                                                                                        equation        ⁢                                  ⁢                  (          1          )                    
Besides, sign(ω1x-h) is a matrix sign function which takes the value +1 when (ωTX-h)>0, and −1 when (ωTx-h)≦0. That is to say, the inner product of vector x, and ω is a two value function which takes the value +1 when it exceeds threshold value h, and −1 when it does not exceed threshold value h.
In addition, said Support Vector Machine is a training machine that helps to train parameters of said linear input elements, that is to say, to update connection weight vector ω based on maximum margin criterion in order to find the separating surface (hyperplane) that has the largest distance between each data points, namely between input feature vector x (magin) by means of training data (training sample), namely a pair of input feature vector x as well as output value y.